Selected Publications

As an ATLAS member, I am an author on 550+ collaboration papers. Here I list those to which I made a major contribution.

Published Papers

  1. T. Jenegger, NH, R. Gernhäuser, L. Fabbietti, L. Heinrich, Machine learning for the cluster reconstruction in the CALIFA calorimeter at R3B, NIM-A, Vol 1082 (2026) 171048, 10.1016/j.nima.2025.171048.

    Contribution: We built a two-step hierarchical (agglomorative) clustering followed by "edge detection" NN to reconstruct calorimeter clusters at the NuStar detector at FAIR (Facility for Antiproton and Ion Research). I mentored Tobias on the ML methods and code implementation.

  2. ATLAS Collaboration. Search for X → SH → b̄bb̄b at √s = 13 TeV with the ATLAS detector. Analysis unblinded, public by end of the year.

    Contribution: First ATLAS analysis to use the normalizing flows background estimate I developed in my PhD. Mentored Thandi Madula (UCL) and Malin Horstmann (TUM) on background estimation, validation, and statistical analysis. Paper contact editor.

  3. ATLAS Collaboration. Transforming Jet Flavour Tagging at ATLAS, Nature Commun. 17 (2026) 541 10.1038/s41467-025-65059-6.

    First end-to-end track-based tagger (GN2, transformer) recommended for physics analyses. Contribution: I led the development of track-based taggers in my PhD with RNNs and Deep Set. All the innovations I introduced for the Deep Set (new variables, optimized track selection) propagated to this SOTA transformer. I also led the team as we finalized GN2 for physics analyses.

  4. M. Vigl, NH, L. Heinrich. Finetuning Foundation Models for Joint Analysis Optimization. MLST 5, 10.1088/2632-2153/ad55a3.

    Summary: Combination of neural networks for an end-to-end optimized analysis (jointly optimizing Higgs jet tagger and downstream event classifier). Proof-of-concept study for X → HH → 4b suggests a 2x improvement in background rejection. Contribution: I found the dataset and reprocessed it with extra variables needed for combined training. Mentored M. Vigl on jet tagging and analysis.

  5. J. Barr, et. al. Umami: A Python toolkit for jet flavour tagging. Journal of Open Source Software, 9(102), 5833, 10.21105/joss.05833.

    Contribution: Software publication for the Deep Sets training workflows from my PhD.

  6. ATLAS Collaboration. High-dimensional background interpolation with normalizing flows and Gaussian Processes on ATLAS. Paper in progress.

    Contribution: I developed novel method (normalizing flow) to interpolate into a blinded signal region; demonstrated better background modelling compared to SOTA method used in prior work.

    Note: the work in this paper has been done since 2022, results in Chapter 13 of my thesis. ATLAS management is requesting we publish a physics result (my postdoc analysis) before publishing this methods paper.

  7. ATLAS Collaboration. "Search for non-resonant pair production of Higgs bosons in the bb̄bb̄ final state using 126 fb⁻¹ of pp collision data at √s = 13 TeV with the ATLAS detector." Phys. Rev. D 108 (2023) 052003, 10.1103/PhysRevD.108.052003.

    Contributions (main paper from my PhD):

    • Optimized analysis selection decreasing the combinatorial background by 70%. These optimizations and better b-taggers improved the analysis sensitivity by 30% compared to what was expected from a larger dataset.
    • Designed new validation regions to provide state-of-the-art understanding of analysis' data-driven modeling uncertainties.
    • Internal note editor: coordinated / summarized the work of O(50) people for analysis review.

  8. ATLAS Collaboration. "ATLAS b-tagging algorithms for the LHC Run 2 dataset." Eur. Phys. J. C 83 (2023) 681 2211.16345.

    Contribution: Optimized Recurrent Neural Network (RNN) tagger: first time RNN was recommended for physics analyses. This new tagger resulted in a 10% improvement in the non-resonant HH→4b analysis.

  9. ATLAS Collaboration. Search for resonant pair production of Higgs bosons in the bb̄bb̄ final state using pp collisions at √s = 13 TeV with the ATLAS detector. Phys. Rev. D 105 (2022) 092002, arXiv: 2202.07288.

    Contribution: Framework support (due to overlap with other work) and analysis discussions.

  10. NH, R. Teixeira de Lima, M. Kagan, on behalf of the ATLAS Collaboration, Deep Sets for Flavor Tagging at the ATLAS Experiment. Proceedings of the 2020 Connecting The Dots Workshop PROC-CDT2020-10, DOI:10.5281/zenodo.4088760.

    Contribution: Developed a new Deep Sets-based tagger. Optimized selection resulted in 2x improvement in background rejection compared to the RNN.

  11. NH, W.-T. Chiu, and R. T. Scalettar. Magnetic Correlations in a Periodic Anderson Model with Non-Uniform Conduction Electron Coordination. Phys Rev. B 93, 235143 (2016), arXiv: 1601.07214.

    Contribution: Implemented the space representation for a quasicrystal given an adjacency matrix, and studied the spin correlations in a Hubbard model system with Markov Chain Monte Carlo simulations. Project funded by NSF REU (UC Davis).

Public Results

These results also represent my work, and have been peer-reviewed via the rigorous ATLAS internal review process.

  1. ATLAS Collaboration. Carpe Datum: Scaling behavior of transformers for heavy hadron flavor identification ATL-SOFT-PUB-2026-002 (2026).

  2. ATLAS Collaboration. GN3: Multi-task, Multi-modal Transformers for Jet Flavour Tagging in ATLAS ATL-PHYS-PUB-2026-001 (2026).

  3. S. Gessner et. al, Design Initiative for a 10 TeV pCM Wakefield Collider, Input for the Update to the European Strategy of Particle Physics, arXiv 2503.20214.

  4. ATLAS Collaboration. Deep Sets based Neural Networks for Impact Parameter Flavour Tagging in ATLAS ATL-PHYS-PUB-2020-014 (2020).

  5. ATLAS Collaboration. "Performance of 2019 recommendations of ATLAS Flavor Tagging algorithms with Variable Radius track jets" FTAG-2019-006 (2019).

  6. ATLAS Collaboration. "Expected performance of the 2019 ATLAS b-taggers" FTAG-2019-005 (2019).

ATLAS Internal Review

On ATLAS papers, the editorial board (EB) carefully reviews an analysis to prepare for publication. I have served on three EBs.

  1. ATLAS Collaboration. A search for non-resonant Higgs boson pair production in the bb̄τ⁺τ⁻ final state using Run 2 + partial Run 3 data recorded by the ATLAS detector. Paper in progress with Editorial Board, pre-unblinding.

  2. Configuration, Performance, and Commissioning of the ATLAS b-jet Triggers for the 2022 and 2023 LHC data-taking periods, JINST 20 (2025) P03002.

  3. ATLAS Collaboration. Search for Hb → 3b at √s = 13 TeV with the ATLAS detector. Paper in progress with Editorial Board, pre-unblinding.